A composite algorithm for variable size object tracking for high performance FPGA-based on-board vision systems

Alpatov, Boris A.; Korepanov, Simon E.; Strotov, Valery V.

doi:10.1117/12.2064058

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…SIFT, SURF, ORB OpenCV implementations were used [7]. We have evaluated the proposed algorithm written in C++ and compared the results to the searching registration, using only CEA.…”

Section: Resultsmentioning

confidence: 99%

Combined Algorithm of Image Registration Using Searching and Tracking Techniques

2015

2015 the 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering

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We analyze the problem of image registration and offer a new approach in which benefits of previously developed approaches are combined. The algorithm involves two techniques: searching and tracking. Feature detection and matching algorithms were applied to perform tracking technique. Correlation-extremal algorithm is used for searching technique. We describe the advantages of the presented idea in experimental results and give some examples which reflect the speed-up and the quality of registration.

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“…SIFT, SURF, ORB OpenCV implementations were used [7]. We have evaluated the proposed algorithm written in C++ and compared the results to the searching registration, using only CEA.…”

Section: Resultsmentioning

confidence: 99%

Combined Algorithm of Image Registration Using Searching and Tracking Techniques

2015

2015 the 5th International Workshop on Computer Science and Engineering-Information Processing and Control Engineering

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“…However, some relatively recent research efforts look promising [4,5]. The alternative approaches based on algorithm switching already applied for ground object detection, for example [6].…”

Section: Introductionmentioning

confidence: 99%

Aerial vehicles detection and recognition for UAV vision system

Muraviev¹,

Smirnov²,

Strotov³

2017

Computer Optics

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This article focuses on aerial vehicle detection and recognition by a wide field of view monocular vision system that can be installed on UAVs (unmanned aerial vehicles). The objects are mostly observed on the background of clouds under regular daylight conditions. The main idea is to create a multi-step approach based on a preliminary detection, regions of interest (ROI) selection, contour segmentation, object matching and localization. The described algorithm is able to detect small targets, but unlike many other approaches is designed to work with large-scale objects as well. The suggested algorithm is also intended to recognize and track the aerial vehicles of specific kind using a set of reference objects defined by their 3D models. For that purpose a computationally efficient contour descriptor for the models and the test objects is calculated. An experimental research on real video sequences is performed. The video database contains different types of aerial vehicles: airplanes, helicopters, and UAVs. The proposed approach shows good accuracy in all case studies and can be implemented in onboard vision systems.

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A composite algorithm for variable size object tracking for high performance FPGA-based on-board vision systems

Cited by 2 publications

References 3 publications

Combined Algorithm of Image Registration Using Searching and Tracking Techniques

Combined Algorithm of Image Registration Using Searching and Tracking Techniques

Aerial vehicles detection and recognition for UAV vision system

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